A Big Data Analytical Framework for Sports Behavior Mining and Personalized Health Services

Mobile healthcare has become an important trend in medical and healthcare domains. With the rapid development of wearable and sensing technologies, various health-related information can now be recorded, forming valuable big health data. Physical activities are considered to have a great impact on heart rate, and the analysis of heart rate data now is widely used in medical/healthcare researches. The analysis of exercise records and heart rate data have been used for the research of the exercise intensity in many institutes. Heart rate patterns refers to a symbol of health status of heart, which is based on the current rate, and other physiological parameters. An effective heart rate pattern discovering is very helpful for the healthcare and cardiovascular prevention. In this work, we aim to build a big data analytics framework for sports behavior mining and personalized health services. We analyzed users' exercise data including heart rate and GPS data, which were collected in a practical sports and social platform, to discover users' periodic sports patterns and the trend of heart rate change during exercise. Since the dataset is not only very huge but also growing very quickly, we adopt Apache Spark as the development framework to address this Velocity issue in Big Data. The analytical results can serve as important core for personalized healthcare applications. Moreover, we also group the individual result to discover the clustering result, which can be further applied for advanced healthcare applications.

[1]  Emanuel Parzen,et al.  Autoregressive Spectral Estimation. , 1983 .

[2]  Vincent S. Tseng,et al.  An interactive healthcare system with personalized diet and exercise guideline recommendation , 2015, 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI).

[3]  Vincent S. Tseng,et al.  Design of a Real-Time and Continua-Based Framework for Care Guideline Recommendations , 2014, International journal of environmental research and public health.

[4]  Miroslav Voznak,et al.  PFPM: Discovering Periodic Frequent Patterns with Novel Periodicity Measures , 2017 .

[5]  Feng Xiao,et al.  Heart Rate Prediction Model Based on Physical Activities Using Evolutionary Neural Network , 2010, 2010 Fourth International Conference on Genetic and Evolutionary Computing.

[6]  Siem Jan Koopman,et al.  Time Series Analysis by State Space Methods , 2001 .

[7]  Vincent S. Tseng,et al.  A Framework for Personalized Diet and Exercise Guideline Recommendation , 2014, TAAI.

[8]  Eric Hsueh-Chan Lu,et al.  Mining temporal mobile sequential patterns in location-based service environments , 2007, 2007 International Conference on Parallel and Distributed Systems.

[9]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[10]  Ching-Yu Chen,et al.  A Novel Complex-Events Analytical System Using Episode Pattern Mining Techniques , 2015, IScIDE.

[11]  Philip S. Yu,et al.  Clustering by pattern similarity in large data sets , 2002, SIGMOD '02.

[12]  Tzung-Pei Hong,et al.  Fuzzy data mining for time-series data , 2012, Appl. Soft Comput..

[13]  Vincent S. Tseng,et al.  A scalable complex event analytical system with incremental episode mining over data streams , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[14]  Eric Hsueh-Chan Lu,et al.  Mining Cluster-Based Mobile Sequential Patterns in Location-Based Service Environments , 2009, 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware.

[15]  Vincent S. Tseng,et al.  MINING TEMPORAL RARE UTILITY ITEMSETS IN LARGE DATABASES USING RELATIVE UTILITY THRESHOLDS , 2008 .